A Practical Approach to Malaria Diagnosis in Non-Endemic Regions: Evaluating Simple Clinical and Laboratory Predictors in Travelers Returning from Endemic Regions.

BACKGROUND

Malaria, a life-threatening parasitic disease, remains a significant global health challenge. Malaria diagnosis in nonendemic regions can be challenging because of limited expertise and resources; rapid and accurate diagnosis is crucial for timely treatment and prevention of disease transmission. To improve diagnostic performance, this study aimed to evaluate the utility of clinical and laboratory parameters as predictors of malaria infection in patients presenting with fever after returning from malaria-endemic areas.

METHODS

A prospective observational hospital-based study with convenience sampling was conducted among febrile patients presenting to the emergency department in Riyadh city/Saudi Arabia with a history of travel to malaria-endemic regions. The detailed clinical information and laboratory parameters, including complete blood count, liver function tests, cholesterol, and lactate dehydrogenase, were collected. Malaria was confirmed by rapid diagnostic tests (RDTs) and microscopic examination of blood smears. The diagnostic accuracy of various clinical and laboratory predictors was assessed via sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios.

FINDINGS

While no single clinical or laboratory predictor was sufficient to definitively diagnose malaria, a combination of these factors proved to be a valuable tool. Low cholesterol (<3 mmol/L) demonstrated high sensitivity, whereas low platelet count (<150 × 10/L) exhibited high specificity. Elevated lactate dehydrogenase (>190 U/L) had the highest sensitivity but lower specificity. A combination of these laboratory markers, along with fever, vomiting, and chills, showed better performance in the diagnosis of malaria.

CONCLUSION

This study highlights the potential of a simple, clinical approach to aid in the diagnosis of malaria in nonendemic settings. A combination of clinical features and laboratory tests can significantly improve diagnostic accuracy, particularly in resource-limited settings. Further validation studies are needed to refine and optimize this approach.